Production and quality measurement are generally separated in semiconductor and TFT-LCD plants, i.e. after first being processed on a production tool, products are then delivered to a measurement tool for inspection. Based on cost consideration, most of the inspection jobs are done by randomly selecting some products in a product lot to determine the product quality. Consequently, occurrence of defects during production process may not usually be discovered until measurement. As such, numerous defective products might have been produced before measurement is performed. Currently, most methods to overcome this problem are to monitor process parameters of the production tool so as to judge whether any product quality defects occur. Those methods are commonly disadvantageous in that defective products have already been produced as soon as the monitoring system detects that the process parameters are abnormal. Currently, most semiconductor and TFT-LCD plants employ batch production, meaning that in the event of defects being discovered, the entire product lot must be discarded, not simply one or two units of products. Therefore, defects not only reduce product yield, but also seriously impact production capacity and cost. Every manufacturer consequently, is eager to find a way to predict production quality of the next product lot.
Currently, several scholars have performed some researches on how to predict product quality or whether the equipment or process is abnormal. Those researches includes: proposing an architecture for improving equipment maintenance work in semiconductor plants and identifying the cause of a defect based on the production data; applying neural networks for real-time fault identification in plasma etching, wherein a pattern recognition technique is used to determine the process number for each record of plasma etching; designing a neurofuzzy system having a graphic user interface for surface mount assembly defect prediction and control by using fuzzy associative memory (FAM) to gather process knowledge in combination with operation management strategy of level coordination, wherein the surface mounting technique (SMT) is used as an example; providing a methodology for extracting wafer-level defect density distributions to improve yield prediction, so as to find the degree of defect wafer clustering, thereby saving the time and cost for data collection and analysis; proposing an approach for reliable life-time prediction of GaAs devices via quantitative measurement of channel temperature; and studying the influence of elevated temperature on degradation and lifetime prediction of thin silicon-dioxide films.
However, the above studies mainly focus on using the available sensor data to identify possible defects of current production, but not the quality of the next product lot. Generally speaking, not many input data can be considered simultaneously for purposes of conjecture in most studies, and the applications of the proposed conjecture schemes are limited to certain types of equipment.
With regard to the existing patent references, U.S. Pat. No. 6,594,542, applied in the semiconductor industry, discloses a system for controlling chemical mechanical polishing thickness removal, the system mainly comprising three parts: a polisher, a thickness measuring device and a polishing rate control system. Based on film thickness comparison, the patent reference predicts the required polishing rate. This patent reference basically still needs to use actual quality measurement values for providing the information required by polishing control. This patent reference cannot conjecture the film thickness by the method of virtual metrology, and the film thickness cannot be determined until measurement.
U.S. Pat. No. 6,625,513, applied in the semiconductor equipment industry, discloses a method of using data-based model to compare the semiconductor tool variations during production processes, and to change the parameter settings in accordance with the comparison result, thereby preventing the defective products produced from tool variations. This method is disadvantageous in that: varieties and difficulty level of the data-based model relatively increase a lot, when there are many process parameters; and the data-based model cannot be properly used in accordance with various tool features, thus not having flexibility.
U.S. Pat. No. 6,616,759, applied in a semiconductor process, discloses a method and a system for monitoring a semiconductor processing apparatus and predicting its processing results. This method collects sensor data of the semiconductor processing apparatus and the measurement values of the process result, and uses a partial least square method to calculate new parameter settings. However, this method is merely based on the existing parameter data to predict the measurement value of the product currently being manufactured, but cannot further predict the quality of the product to be produced in a future period of time.
U.S. Pat. No. 6,666,577, applied in wafer temperature prediction, discloses a method for predicting wafer temperature. This method uses two different coating films formed on the wafer to predict the wafer temperature. However, the method disclosed in this patent reference basically can be merely applicable to certain types of equipment, and lacks of generic applicability.
Hence, there is an urgent need to develop a quality prognostics system and a quality prognostics method for manufacturing processes, thereby predicting the quality of a next product lot before the next product lot is produced by using the current process parameters of a production tool and the actual measurement values of several previous product lots produced in the measurement tool; and having generic applicability, thus further reducing the shortcomings of the conventional skills.